53e0065e3e
配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
526 lines
19 KiB
Python
526 lines
19 KiB
Python
"""评测台评审：智谱 / DeepSeek 等 OpenAI 兼容端点（结构化 JSON）。"""
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from __future__ import annotations
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from collections.abc import AsyncIterator
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from dataclasses import dataclass
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from typing import Any, Generic, TypeVar
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from app.core.config import settings
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from app.core.eval_judge_spec import EvalJudgeProvider
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from app.core.llm_call import LLMCallError, allm_json_call
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from app.core.logging import get_logger
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from app.features.evaluation.judge_schemas import (
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ConversationJudgeOutput,
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MemoirJudgeOutput,
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TurnJudgeOutput,
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)
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from app.features.evaluation.rubrics.conversation_v1 import (
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COMPARE_CONV_STREAM_HINT,
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CONV_JUDGE_INSTRUCTIONS,
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TURN_JUDGE_INSTRUCTIONS,
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)
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from app.features.evaluation.rubrics.memoir_v1 import MEMOIR_JUDGE_INSTRUCTIONS
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from app.features.evaluation.constants import eval_cfg
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from app.features.memoir.constants import memoir
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logger = get_logger(__name__)
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TJudgeOutput = TypeVar(
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"TJudgeOutput", TurnJudgeOutput, ConversationJudgeOutput, MemoirJudgeOutput
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)
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_TURN_MAX = 768
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_CONV_JUDGE_JSON_MAX = 2048
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_CONV_HEADER = "【完整对话】（每轮以 `[Turn k]` 开头）\n\n"
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_COMPARE_STREAM_MAX = 6144
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def _eval_judge_prompt_char_pool_for_context(context_window_tokens: int) -> int:
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"""整段请求的字符预算（由评审模型 context window 推导，保守）。"""
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toks = (
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int(context_window_tokens)
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- eval_cfg.judge_completion_reserve_tokens
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- eval_cfg.judge_prompt_budget_safety_tokens
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)
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toks = max(1, toks)
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return max(1, int(toks / eval_cfg.judge_approx_tokens_per_char))
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def _eval_judge_prompt_char_pool() -> int:
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return _eval_judge_prompt_char_pool_for_context(
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eval_cfg.judge_context_window_tokens
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)
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def eval_judge_conversation_transcript_max_chars() -> int:
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"""整段对话评审：【完整对话】transcript 最大字符数（默认 GLM 上下文）。"""
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if eval_cfg.judge_max_transcript_chars > 0:
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return eval_cfg.judge_max_transcript_chars
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overhead = len(CONV_JUDGE_INSTRUCTIONS) + len(_CONV_HEADER) + 32
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return max(1, _eval_judge_prompt_char_pool() - overhead)
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def eval_judge_conversation_transcript_max_chars_for_context(
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context_window_tokens: int,
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) -> int:
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if eval_cfg.judge_max_transcript_chars > 0:
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return eval_cfg.judge_max_transcript_chars
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overhead = len(CONV_JUDGE_INSTRUCTIONS) + len(_CONV_HEADER) + 32
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pool = _eval_judge_prompt_char_pool_for_context(context_window_tokens)
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return max(1, pool - overhead)
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def eval_judge_turn_prior_transcript_max_chars() -> int:
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"""逐轮评审：截至上一轮的 transcript 节选上限（默认 GLM 上下文）。"""
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if eval_cfg.judge_max_transcript_chars > 0:
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return eval_cfg.judge_max_transcript_chars
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static = len(TURN_JUDGE_INSTRUCTIONS) + 8800
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return max(1, _eval_judge_prompt_char_pool() - static)
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def eval_judge_turn_prior_transcript_max_chars_for_context(
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context_window_tokens: int,
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) -> int:
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if eval_cfg.judge_max_transcript_chars > 0:
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return eval_cfg.judge_max_transcript_chars
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static = len(TURN_JUDGE_INSTRUCTIONS) + 8800
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pool = _eval_judge_prompt_char_pool_for_context(context_window_tokens)
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return max(1, pool - static)
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def eval_judge_compare_transcript_each_max_chars() -> int:
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"""单侧对称参考上限（默认与 eval_cfg.judge_context_window_tokens 一致）。"""
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return eval_judge_compare_transcript_each_max_chars_for_context(
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eval_cfg.judge_context_window_tokens
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)
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def eval_judge_compare_transcript_pair_total_budget_for_context(
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context_window_tokens: int,
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) -> int:
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"""A/B 同 prompt 时，两份 transcript 合计最大字符数（已扣对比模板与双份 JSON 等开销）。"""
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if eval_cfg.judge_max_compare_transcript_chars_each > 0:
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return max(1, 2 * int(eval_cfg.judge_max_compare_transcript_chars_each))
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pool = _eval_judge_prompt_char_pool_for_context(context_window_tokens)
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return max(1, pool - int(eval_cfg.judge_compare_prompt_overhead_chars))
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def eval_judge_compare_transcript_each_max_chars_for_context(
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context_window_tokens: int,
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) -> int:
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"""单侧对称上限的参考值（auto 模式下约为合计预算的一半；供兼容与展示）。"""
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if eval_cfg.judge_max_compare_transcript_chars_each > 0:
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return int(eval_cfg.judge_max_compare_transcript_chars_each)
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total = eval_judge_compare_transcript_pair_total_budget_for_context(
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context_window_tokens
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)
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return max(1, total // 2)
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def eval_judge_compare_bundle_caps(
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context_window_tokens: int,
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) -> tuple[int, int | None]:
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"""返回 (compare_cap_total, per_side_cap|None)，供 Playground 摘要与流式对比共用。"""
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per = int(eval_cfg.judge_max_compare_transcript_chars_each or 0)
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if per > 0:
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return max(1, 2 * per), per
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return eval_judge_compare_transcript_pair_total_budget_for_context(
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context_window_tokens
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), None
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def trim_compare_transcript_pair(
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baseline: str,
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replay: str,
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*,
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total_max_chars: int,
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per_side_max_chars: int | None = None,
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) -> tuple[str, str, bool, bool]:
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"""A/B 对比 prompt 用：在合计预算内尽量保留全文；仅超长时优先从较长的一侧裁尾部。
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若配置了 eval_judge_max_compare_transcript_chars_each，则仍按单侧硬顶（与旧行为一致）。
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"""
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b = (baseline or "").strip()
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r = (replay or "").strip()
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if per_side_max_chars is not None and int(per_side_max_chars) > 0:
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cap = int(per_side_max_chars)
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return b[:cap], r[:cap], len(b) > cap, len(r) > cap
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cap_total = max(1, int(total_max_chars))
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if len(b) + len(r) <= cap_total:
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return b, r, False, False
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need_drop = len(b) + len(r) - cap_total
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b2, r2 = b, r
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while need_drop > 0 and (b2 or r2):
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if len(b2) >= len(r2):
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if b2:
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b2 = b2[:-1]
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need_drop -= 1
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elif r2:
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r2 = r2[:-1]
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need_drop -= 1
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else:
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break
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else:
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if r2:
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r2 = r2[:-1]
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need_drop -= 1
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elif b2:
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b2 = b2[:-1]
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need_drop -= 1
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else:
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break
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return b2, r2, len(b) > len(b2), len(r) > len(r2)
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_CONV_JUDGE_TRANSCRIPT_TRUNCATION_TAIL = (
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"\n\n【评审边界——输入已为截断稿】\n"
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"以上仅为全文前 {n} 个字符，其后未提供给模型。"
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"对依赖长程多轮轨迹的细项（尤其 context_memory、interview_structure、跨轮重复盘问）"
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"必须保守给分（倾向区间中低），并在 insufficient_evidence 写明「输入为截断稿，长程证据不足」；"
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"不得臆断未展示轮次中的行为；confidence 须显著降低；禁止因未见问题而默认高分或推断后半段无缺陷。\n"
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)
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_TURN_PRIOR_TRUNCATION_TAIL = (
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"\n\n【评审边界——上文节选已截断】\n"
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"「截至上一轮」节选可能仅为更长对话的前 {n} 字；跨轮重复、长程结构若无法从节选核实，"
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"须在 insufficient_evidence 说明，并对相关细项保守给分。\n"
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)
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_COMPARE_STREAM_PAIR_TRUNCATION_NOTE = (
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"\n【评审边界】以下 A/B transcript 至少一侧为截断稿，请仅就**已展示片段**比较；"
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"不得断言未展示轮次的优劣；涉及跨轮重复盘问等须明确证据范围或说不足以判断。\n"
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)
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def conversation_judge_transcript_excerpt(full_transcript: str, cap: int) -> str:
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"""整段评审：在 cap 内截断时在正文后附加边界说明，减少「假装看了全文」的幻觉打分。"""
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raw = (full_transcript or "").strip()
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c = max(0, int(cap))
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if len(raw) <= c:
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return raw
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return raw[:c] + _CONV_JUDGE_TRANSCRIPT_TRUNCATION_TAIL.format(n=c)
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def turn_judge_prior_excerpt(prior_transcript: str, cap: int) -> str:
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"""逐轮评审里「截至上一轮」节选；截断时附加边界说明。"""
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raw = (prior_transcript or "").strip()
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c = max(0, int(cap))
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if len(raw) <= c:
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return raw
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return raw[:c] + _TURN_PRIOR_TRUNCATION_TAIL.format(n=c)
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@dataclass(slots=True)
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class JudgeCallResult(Generic[TJudgeOutput]):
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output: TJudgeOutput | None
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error: str | None = None
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def _judge_error_message(e: LLMCallError) -> str:
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prefix = {
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"invoke": "模型调用失败",
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"decode": "JSON 解析失败",
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"validation": "结果校验失败",
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}.get(e.kind, "评审失败")
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detail = str(e).strip()
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return f"{prefix}: {detail}" if detail else prefix
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def _build_memoir_judge_prompt(
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*,
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memoir_markdown: str,
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source_transcript: str = "",
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structured_evidence: str = "",
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reference_memoir_markdown: str = "",
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evidence_notes: str = "",
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) -> str:
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"""Assemble an evidence-aware memoir judging prompt."""
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source = (source_transcript or "").strip()
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struct = (structured_evidence or "").strip()
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reference = (reference_memoir_markdown or "").strip()
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notes = (evidence_notes or "").strip()
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sections = [
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MEMOIR_JUDGE_INSTRUCTIONS,
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"",
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"【证据与输入顺序】以下区块按优先级给出："
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"评审说明（若有）→ 原始访谈/对话证据（segment 绑定）→ 结构化记忆证据（chunk/fact/timeline/summary）"
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"→ 参考基线（若有）→ 待评成稿。**真实性、覆盖率、可追溯性以「artifact 绑定证据闭包」为准**；"
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"若证据不足，须保守打分并写 `insufficient_evidence`。",
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"",
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]
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ev_cap = max(1, int(eval_cfg.judge_memoir_evidence_max_chars))
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body_cap = max(1, int(eval_cfg.judge_memoir_body_max_chars))
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if notes:
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sections.extend(["【评审说明】", notes[:1200], ""])
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if source:
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sections.extend(["【原始访谈/对话证据】", source[:ev_cap], ""])
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else:
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sections.extend(
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[
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"【原始访谈/对话证据】",
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"无可用局部对话证据。对于记忆忠实度、事实准确性、事实覆盖率、记忆可追溯性，必须保守打分，不得凭空高分。",
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"",
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]
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)
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if struct:
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sections.extend(["【结构化记忆证据】", struct[:ev_cap], ""])
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else:
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sections.extend(
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[
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"【结构化记忆证据】",
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"（本 artifact 未绑定或未解析到 chunk/fact/timeline/summary 证据。）",
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"",
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]
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)
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if reference:
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sections.extend(["【参考基线/导出成稿】", reference[:ev_cap], ""])
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sections.extend(["【当前回忆录正文】", memoir_markdown[:body_cap]])
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return "\n".join(sections)
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class EvalJudgeService:
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def __init__(
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self,
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judge_llm: Any | None,
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*,
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context_window_tokens: int | None = None,
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http_error_vendor: EvalJudgeProvider = "deepseek",
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) -> None:
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self._llm = judge_llm
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self._http_error_vendor: EvalJudgeProvider = http_error_vendor
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self._ctx_tokens = int(
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context_window_tokens or eval_cfg.judge_context_window_tokens
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)
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def _conv_transcript_cap(self) -> int:
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return eval_judge_conversation_transcript_max_chars_for_context(
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self._ctx_tokens
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)
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def _turn_prior_cap(self) -> int:
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return eval_judge_turn_prior_transcript_max_chars_for_context(self._ctx_tokens)
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async def judge_turn(
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self,
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*,
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prior_transcript: str,
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user_utterance: str,
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assistant_reply: str,
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turn_index_0: int = 0,
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) -> TurnJudgeOutput | None:
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if not self._llm:
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return None
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t = max(0, int(turn_index_0))
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prompt = f"""{TURN_JUDGE_INSTRUCTIONS}
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【本轮位置】完整对话中当前轮次为 Turn {t + 1}（与下方节选及全量 transcript 的 `[Turn ...]` 编号一致）。evidence_refs.turn_index 请使用该编号。
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【截至上一轮的对话节选】（含 `[Turn k]` 标签）
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{turn_judge_prior_excerpt(prior_transcript, self._turn_prior_cap())}
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【本轮用户】
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{user_utterance[:4000]}
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【本轮 AI】
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{assistant_reply[:4000]}
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"""
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try:
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return await allm_json_call(
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self._llm,
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prompt,
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TurnJudgeOutput,
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max_tokens=_TURN_MAX,
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agent="EvalJudgeService.judge_turn",
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http_error_vendor=self._http_error_vendor,
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)
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except LLMCallError as e:
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logger.warning("turn judge failed: {}", e)
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return None
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async def judge_conversation_result(
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self, *, full_transcript: str
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) -> JudgeCallResult[ConversationJudgeOutput]:
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if not self._llm:
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return JudgeCallResult(
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output=None,
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error="评审模型未配置（智谱或 DeepSeek 密钥）",
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)
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prompt = f"""{CONV_JUDGE_INSTRUCTIONS}
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【完整对话】（每轮以 `[Turn k]` 开头）
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{conversation_judge_transcript_excerpt(full_transcript, self._conv_transcript_cap())}
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"""
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try:
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out = await allm_json_call(
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self._llm,
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prompt,
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ConversationJudgeOutput,
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max_tokens=_CONV_JUDGE_JSON_MAX,
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agent="EvalJudgeService.judge_conversation",
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http_error_vendor=self._http_error_vendor,
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)
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return JudgeCallResult(output=out)
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except LLMCallError as e:
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error = _judge_error_message(e)
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logger.warning("conversation judge failed: {}", error)
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return JudgeCallResult(output=None, error=error)
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async def judge_conversation(
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self, *, full_transcript: str
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) -> ConversationJudgeOutput | None:
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result = await self.judge_conversation_result(full_transcript=full_transcript)
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return result.output
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async def stream_conversation_compare(
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self,
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*,
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baseline_transcript: str,
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replay_transcript: str,
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baseline_judge: ConversationJudgeOutput | None,
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replay_judge: ConversationJudgeOutput | None,
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) -> AsyncIterator[str]:
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"""流式输出中文对比与建议（非 JSON）。"""
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if not self._llm:
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yield "[错误] 未配置评审模型 API Key（智谱：ZHIPU_API_KEY；DeepSeek：DEEPSEEK_API_KEY）"
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return
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cap_total, per_side = eval_judge_compare_bundle_caps(self._ctx_tokens)
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cap_single = self._conv_transcript_cap()
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b_tr, r_tr, b_cmp_trunc, r_cmp_trunc = trim_compare_transcript_pair(
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baseline_transcript or "",
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replay_transcript or "",
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total_max_chars=cap_total,
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per_side_max_chars=per_side,
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)
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compare_pair_truncated = b_cmp_trunc or r_cmp_trunc
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b_json = (
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baseline_judge.model_dump_json(ensure_ascii=False)
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if baseline_judge
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else "null"
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)
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r_json = (
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replay_judge.model_dump_json(ensure_ascii=False) if replay_judge else "null"
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)
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if baseline_judge and replay_judge:
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trunc_line = (
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_COMPARE_STREAM_PAIR_TRUNCATION_NOTE if compare_pair_truncated else ""
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)
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prompt = f"""你是访谈对话评测专家。下面给出两份对话 transcript 及各自的整体打分（JSON）。请用中文直接写正文（不要用 JSON、不要用 Markdown 代码块）：
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【A：导出基准对话】（历史快照：用户与当时导出的线上 AI，多轮合并为一篇）
|
||
{b_tr}
|
||
|
||
【B：本次回放/新测对话】（用户句与基准对齐，AI 为当前后端重新生成）
|
||
{r_tr}
|
||
{trunc_line}
|
||
【A 的整体评分 JSON】
|
||
{b_json}
|
||
|
||
【B 的整体评分 JSON】
|
||
{r_json}
|
||
|
||
请依次撰写：
|
||
1) 两段对话在整体体验上的主要差异（情绪承接、信息挖掘、人物建模、访谈结构、提问质量、上下文与重复盘问等）；
|
||
2) B 相对 A 的优点与不足；
|
||
3) 若 B 在关键维度明显弱于 A，给出可操作的改进方向（系统提示、访谈策略、模型或温度等）。
|
||
|
||
笔调简洁、偏执行清单。"""
|
||
elif replay_judge:
|
||
r_one = conversation_judge_transcript_excerpt(
|
||
replay_transcript or "", cap_single
|
||
)
|
||
prompt = f"""{COMPARE_CONV_STREAM_HINT}
|
||
|
||
【回放/新测 transcript】
|
||
{r_one}
|
||
|
||
【整体评分 JSON】
|
||
{r_json}
|
||
"""
|
||
else:
|
||
yield "[错误] 缺少回放对话评分，无法生成建议"
|
||
return
|
||
|
||
llm = self._llm
|
||
if hasattr(llm, "bind"):
|
||
llm = llm.bind(max_tokens=_COMPARE_STREAM_MAX)
|
||
try:
|
||
from app.core.llm_telemetry import infer_provider_model, observe_astream
|
||
|
||
provider, model = infer_provider_model(llm, http_error_vendor="zhipu")
|
||
async for chunk in observe_astream(
|
||
llm,
|
||
prompt,
|
||
agent="EvalJudge.stream_conversation_compare",
|
||
provider=provider,
|
||
model=model,
|
||
):
|
||
piece = getattr(chunk, "content", None)
|
||
if piece:
|
||
yield piece
|
||
except Exception as e:
|
||
logger.warning("conversation compare stream failed: {}", e)
|
||
yield f"\n\n[流式输出中断：{e}]"
|
||
|
||
async def judge_memoir(
|
||
self,
|
||
*,
|
||
memoir_markdown: str,
|
||
source_transcript: str = "",
|
||
structured_evidence: str = "",
|
||
reference_memoir_markdown: str = "",
|
||
evidence_notes: str = "",
|
||
) -> MemoirJudgeOutput | None:
|
||
result = await self.judge_memoir_result(
|
||
memoir_markdown=memoir_markdown,
|
||
source_transcript=source_transcript,
|
||
structured_evidence=structured_evidence,
|
||
reference_memoir_markdown=reference_memoir_markdown,
|
||
evidence_notes=evidence_notes,
|
||
)
|
||
return result.output
|
||
|
||
async def judge_memoir_result(
|
||
self,
|
||
*,
|
||
memoir_markdown: str,
|
||
source_transcript: str = "",
|
||
structured_evidence: str = "",
|
||
reference_memoir_markdown: str = "",
|
||
evidence_notes: str = "",
|
||
) -> JudgeCallResult[MemoirJudgeOutput]:
|
||
if not self._llm:
|
||
return JudgeCallResult(
|
||
output=None,
|
||
error="评审模型未配置（智谱或 DeepSeek 密钥）",
|
||
)
|
||
prompt = _build_memoir_judge_prompt(
|
||
memoir_markdown=memoir_markdown,
|
||
source_transcript=source_transcript,
|
||
structured_evidence=structured_evidence,
|
||
reference_memoir_markdown=reference_memoir_markdown,
|
||
evidence_notes=evidence_notes,
|
||
)
|
||
try:
|
||
out = await allm_json_call(
|
||
self._llm,
|
||
prompt,
|
||
MemoirJudgeOutput,
|
||
max_tokens=max(
|
||
512, int(eval_cfg.judge_memoir_completion_max_tokens)
|
||
),
|
||
agent="EvalJudgeService.judge_memoir",
|
||
http_error_vendor=self._http_error_vendor,
|
||
)
|
||
return JudgeCallResult(output=out)
|
||
except LLMCallError as e:
|
||
error = _judge_error_message(e)
|
||
# 回忆录评审在 INFO 也要可见（eval-web 排障）；非异常路径、不刷堆栈
|
||
logger.info(
|
||
"event=eval_memoir_judge_llm_call_failed agent=EvalJudgeService.judge_memoir msg={}",
|
||
error,
|
||
)
|
||
return JudgeCallResult(output=None, error=error)
|